CN102799938B - Optimizing method of 9% martensite steel pipeline postweld heat treatment heating width - Google Patents
Optimizing method of 9% martensite steel pipeline postweld heat treatment heating width Download PDFInfo
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Abstract
The invention relates to an optimizing method of 9% martensite steel pipeline postweld heat treatment heating width. According to the optimizing method, pipeline postweld heat treatment inner-outer wall temperature difference size data of T groups of pipelines with different sizes under different heating widths, different heat treatment environment temperature and different temperature control conditions are obtained through calculation. The minimum heating widths required by pipeline postweld heat treatment of the pipelines under different heat treatment environment temperatures, different temperature control temperatures and different preset inner-outer wall temperature differences are comprehensively considered, so that an error back propagation-based neural network is established for training and testing the minimum heating widths, pipeline size, heat treatment environment temperature, temperature control temperature and preset inner-outer wall temperature difference are used as inputs, and heating widths are used as outputs. By combining with actually measured data of the pipeline postweld heat treatment, a trained and tested network output threshold is corrected to obtain the optimizing method. According to the optimizing method, the minimum heating widths required by the postweld heat treatment can be rapidly calculated, a heat treatment process can be helped to be guided and optimized, and the heat treatment quality is improved.
Description
Technical field
The present invention relates to the optimization method of 9%Cr martensite steel pipeline post weld heat treatment width of heating.
Background technology
9%Cr novel martensitic heat-resisting steel mainly comprises T/P92, T/P91 and E911 tri-kinds of novel martensitic heat-resisting steel, be widely used in the components such as posted sides pipeline such as ultra-supercritical boiler main steam pipe, header, welding seam toughness is on the low side is the subject matter occurred in this Series Steel pipe welding seam installation process.In order to improve welding seam toughness, local heat treatmet must be carried out by butt welded seam.Domestic and international research shows, the impact of post weld heat treatment temperature butt welded seam is very large, (the note: by the restriction of weld seam transformation temperature when heat treatment temperature is at 760 ± 10 DEG C, heat treatment temperature is difficult to improve further), through constant temperature process in short-term, the ballistic work of weld seam just can reach more than 41J, when about 740 DEG C heating, reach this index and must extend constant temperature time, when heating-up temperature is below 730 DEG C, extend constant temperature time more not only to have little effect, ballistic work is difficult to the toughness index reaching 41J, and significantly increase installation cost, have a strong impact on construction speed.
At present, the post weld heat treatment technical regulation of bearing pipe is proposed both at home and abroad on the basis of traditional heat-resisting steel, 9%Cr novel martensitic heat-resisting steel is more harsh for the control of the inside and outside wall temperature difference, therefore these specifications are not necessarily suitable for for the post weld heat treatment of 9%Cr novel martensitic heat-resisting steel, and namely the applicability of existing standard to 9%Cr novel martensitic heat-resisting steel needs to be investigated.
In addition, domestic and international post weld heat treatment specification chooses the very large dispute of upper existence for width of heating, and the width of heating numerical value difference according to different specification gained is very large.This gives during heat treatment on spot and brings a difficult problem, and the applicability of code exists query.
Artificial neural network is the nonlinear science the 80's Mos starting to develop rapidly, artificial nerve network model has very strong fault-tolerance, study property, adaptivity and nonlinear mapping ability, is particularly suitable for the problems such as the Uncertainty Reasoning of solution cause-effect relationship complexity, judgement, identification and classification.At present, most widely used in field of steel metallurgy is the model (BP model) having Multi-layered Feedforward Networks structure and adopt back-propagation training method.
Summary of the invention
The present invention mainly solves the technical matters existing for prior art; Provide a kind of method can not only optimizing 9%Cr novel martensitic heat-resisting steel posted sides pipeline post weld heat treatment width of heating, guarantee thermal treatment quality, raising heat treatment efficiency tool are of great significance.
The present invention has an object to be solve the problem existing for prior art again; Provide a kind of otherness solving domestic and international heat treatment technics code and 9%Cr novel martensitic heat-resisting steel pipeline post weld heat treatment width of heating is chosen.
Above-mentioned technical matters of the present invention is mainly solved by following technical proposals:
The optimization method of 9%Cr martensite steel pipeline post weld heat treatment width of heating, is characterized in that, comprise the following steps:
Step 1, Temperature calculating module, the heat place calculation model for temperature field of T group different size pipeline under different heating width, different heat treatment environment temperature, different control temperature in foundation, adopts finite element analysis software to calculate the post weld heat treatment inside and outside wall temperature difference (insulation width is determined by electric power standard) of each group model;
Step 2, neural network module, considers any specification (caliber and wall thickness) pipeline under different heat treatment environment temperature, different control temperature and different default inside and outside wall temperature difference condition, width of heating minimum needed for pipeline.Set up based on error backward propagation method;
Step 3, forecast model sets up module, the data obtaining T group width of heating for step 1 carry out training and testing in step 2 based on error backward propagation method, obtain the forecast model that can be predicted 9%Cr martensite heat-resistant steel posted sides pipeline post weld heat treatment width of heating;
Step 4, Modifying model module, revises in conjunction with the forecast model of 9%Cr martensite heat-resistant steel posted sides pipeline post weld heat treatment measured data of experiment to gained;
Step 5, width of heating optimizes module, and analysis conduit size (caliber and wall thickness), heat treatment environment temperature, control temperature, the default inside and outside wall temperature difference, be input to the minimum width of heating that revised model can obtain pipeline post weld heat treatment.
At the optimization method of above-mentioned 9%Cr martensite steel pipeline post weld heat treatment width of heating, in described step 1, the heat place calculation model for temperature field of T group different size pipeline under different heating width, different heat treatment environment temperature, different control temperature in foundation, pipeline post weld heat treatment inside and outside wall temperature extent under utilization finite element software calculating different condition, concrete grammar is:
According to the applicable cases of 9%Cr novel martensitic heat-resisting steel, choose line size scope; According to domestic and international heat treatment technics code, the pipeline for certain specification calculates the size of heating tape width, insulation width, chooses width of heating scope, and insulation width is chosen according to electric power standard; According to control temperature and the heat treatment environment temperature conditions of 9%Cr novel martensitic heat-resisting steel, select the scope of control temperature and heat treatment environment temperature.Set up T group 9%Cr novel martensitic heat-resisting steel pipeline post weld heat treatment temperature field theoretical calculation model, by using finite element software to calculate line size (caliber and wall thickness), width of heating, control temperature and heat treatment environment temperature to the impact of equivalent point position, computing method are as follows:
Step 1.1, in finite element software, sets up 9%Cr novel martensitic heat-resisting steel post weld heat treatment calculation model for temperature field;
Step 1.2, definition starting condition, boundary condition, solve;
Step 1.3, after having calculated, checks inner-walls of duct temperature and outside wall temperature in preprocessor, calculates inside and outside wall temperature extent.
At the optimization method of above-mentioned 9%Cr martensite steel pipeline post weld heat treatment width of heating, in described step 2, the concrete grammar set up based on error backward propagation method is:
Step 2.1, definition input layer and output layer
Choose line size (caliber and wall thickness), preset the numerical value of the inside and outside wall temperature difference, control temperature and heat treatment environment temperature as input variable, therefore the neuron number of this network input layer is 5; Minimum width of heating required under different condition is as the output of network model, and therefore output layer neuron number is 1.
Step 2.2, selects hidden layer number and Hidden unit number: adopt single hidden layer, and determine that the number of hidden nodes is 10.
Step 2.3, the determination of other parameters: the transport function of hidden layer hidden layer is unipolarity S type function: f (x)=1/ (1+e
-x), the transport function of output layer is linear function: f (x)=x, makes network export any value, and frequency of training is 1800 times, and error target is 0.5, and selection sample number is T, wherein N number of training sample, T-N test sample book.
At the optimization method of above-mentioned 9%Cr martensite steel pipeline post weld heat treatment width of heating, in described step 2, an input layer, a middle layer and an output layer is comprised based on error backward propagation method, input layer has 5 neurons, there are 10 neurons in middle layer, and output layer has 1 neuron; The transport function in the middle layer of described forecast model is unipolarity S type function, and the transport function of output layer is linear function, makes network export any value; T width of heating is obtained to step 1 as follows to carrying out the concrete steps of training and testing based on error backward propagation method in step 2:
Step 3.1, setting weights and threshold and frequency of training, and initialization is carried out to weights and threshold, win T-N group sample in T group sample at random as training sample, N group sample is as test sample book, input T-N group training sample, described sample is the size of the T group width of heating obtained in step 1 and the influence factor of the minimum width of heating of T group 9%Cr martensite heat-resistant steel pipeline post weld heat treatment;
Step 3.2, computational grid exports, and obtains weights and the threshold value of each layer in reverse transmittance nerve network, and calculates the weights of each layer and the modifying factor of threshold value in reverse transmittance nerve network, according to the T-N group A obtained in step 1
1temperature calculations and network export computational grid output error, and described network output error is the comparison difference of the calculated value of the T-N group width of heating obtained in step 1 and the network output of this step calculating;
Step 3.3, judges whether to reach maximum frequency of training, and according to whether reaching maximum frequency of training selection execution following steps:
Select to perform step 1, if not yet reach maximum frequency of training, judge whether network output error is less than anticipation error in step 3.2, if be less than anticipation error, then train end, preserve weights and the threshold value of each layer in reverse transmittance nerve network in step 3.2 simultaneously, obtain forecast model undetermined; If be greater than anticipation error, after revising the weights of each layer in reverse transmittance nerve network and threshold value step repeat 3.2. wherein modifying factor adopt the modifying factor calculated in step 3.2;
Select to perform step 2, if reach maximum frequency of training, then this reverse transmittance nerve network can not be restrained in given frequency of training, and training terminates;
Step 3.4, by the forecast model undetermined in N group test sample book one by one input selection execution step 1, if predicated error is lower than showing during prescribed level that this forecast model undetermined can be used in predicting the minimum width of heating needed for the post weld heat treatment of 9%Cr martensite heat-resistant steel pipeline, namely namely this forecast model undetermined is the forecast model obtained in step 3; Otherwise this forecast model undetermined does not meet, and terminates whole step.
At the optimization method of above-mentioned 9%Cr martensite steel pipeline post weld heat treatment width of heating, in described step 4, the data of 9%Cr novel martensitic heat-resisting steel posted sides pipeline post weld heat treatment experiment measuring and model calculation value are analyzed, and correction model exports threshold values.
Therefore, tool of the present invention has the following advantages: 1. the method can optimizing 9%Cr novel martensitic heat-resisting steel posted sides pipeline post weld heat treatment width of heating, is of great significance guarantee thermal treatment quality, raising heat treatment efficiency tool; 2. solve the otherness that domestic and international heat treatment technics code is chosen for 9%Cr novel martensitic heat-resisting steel pipeline post weld heat treatment width of heating.
Accompanying drawing explanation
The BP neural network model figure used in Fig. 1 the present invention.
BP neural metwork training process flow diagram in Fig. 2 the present invention.
BP neural metwork training Error Graph in Fig. 3 the present invention.
Embodiment
Below by embodiment, and by reference to the accompanying drawings, technical scheme of the present invention is described in further detail.
The optimization method of 9%Cr martensite steel pipeline post weld heat treatment width of heating of the present invention, comprises the following steps:
Step 1, Temperature calculating module, the heat place calculation model for temperature field of T group different size pipeline under different heating width, different heat treatment environment temperature, different control temperature in foundation, adopt finite element analysis software to calculate the post weld heat treatment inside and outside wall temperature difference (insulation width is determined by electric power standard) of each group model, concrete grammar is:
According to the applicable cases of 9%Cr novel martensitic heat-resisting steel, choose line size scope; According to domestic and international heat treatment technics code, the pipeline for certain specification calculates the size of heating tape width, insulation width, chooses width of heating scope, and insulation width is chosen according to electric power standard; According to control temperature and the heat treatment environment temperature conditions of 9%Cr novel martensitic heat-resisting steel, select the scope of control temperature and heat treatment environment temperature.Set up T group 9%Cr novel martensitic heat-resisting steel pipeline post weld heat treatment temperature field theoretical calculation model, by using finite element software to calculate line size (caliber and wall thickness), width of heating, control temperature and heat treatment environment temperature to the impact of equivalent point position, computing method are as follows:
Step 1.1, in finite element software, sets up 9%Cr novel martensitic heat-resisting steel post weld heat treatment calculation model for temperature field;
Step 1.2, definition starting condition, boundary condition, solve;
Step 1.3, after having calculated, checks inner-walls of duct temperature and outside wall temperature in preprocessor, calculates inside and outside wall temperature extent.
Step 2, neural network module, considers any specification (caliber and wall thickness) pipeline under different heat treatment environment temperature, different control temperature and different default inside and outside wall temperature difference condition, width of heating minimum needed for pipeline.Set up based on error backward propagation method, concrete grammar is:
1) design of input layer and output layer
Choose line size (caliber and wall thickness), preset the numerical value of the inside and outside wall temperature difference, control temperature and heat treatment environment temperature as input variable, therefore the neuron number of this network input layer is 5; Minimum width of heating under different condition needed for pipeline post weld heat treatment is as the output of network model, and therefore output layer neuron number is 1.
2) selection of hidden layer number and Hidden unit number
1989, Robert Hecht-Nielson demonstrated and can approach with the BP network of a hidden layer for the continuous function of in any closed interval.Because the BP network of 3 layers can complete the Continuous Mappings that arbitrary n ties up m dimension, therefore this model adopts single hidden layer, and the selection of the number of hidden nodes is the problem of a more complicated, repeatedly attempts through author in conjunction with experimental formula, finally determine that the number of hidden nodes is 10.
3) determination of other parameters
The transport function of hidden layer hidden layer is unipolarity S type function: f (x)=1/ (1+e
-x), the transport function of output layer is linear function: f (x)=x, makes network export any value, and frequency of training is 1800 times, and error target is 1, and selection sample number is T, wherein N number of training sample, T-N test sample book.
In this step, comprise an input layer, a middle layer and an output layer based on error backward propagation method, input layer has 5 neurons, and there are 10 neurons in middle layer, and output layer has 1 neuron; The transport function in the middle layer of described forecast model is unipolarity S type function, and the transport function of output layer is linear function, and make network export any value, structural drawing as shown in Figure 1.
Step 3, forecast model sets up module, the data obtaining T group width of heating for step 1 carry out training and testing in step 2 based on error backward propagation method, obtain the forecast model that can be predicted 9%Cr martensite heat-resistant steel posted sides pipeline post weld heat treatment width of heating; The T group width of heating data obtained for step 1 are as follows to carrying out the concrete steps of training and testing based on error backward propagation method in step 2:
Step 3.1, setting weights and threshold and frequency of training, and initialization is carried out to weights and threshold, win T-N group sample in T group sample at random as training sample, N group sample is as test sample book, input T-N group training sample, described sample is the size of the T group width of heating obtained in step 1 and the influence factor of the minimum width of heating of T group 9%Cr martensite heat-resistant steel pipeline post weld heat treatment;
Step 3.2, computational grid exports, and obtains weights and the threshold value of each layer in reverse transmittance nerve network, and calculates the weights of each layer and the modifying factor of threshold value in reverse transmittance nerve network, according to the T-N group A obtained in step 1
1temperature calculations and network export computational grid output error, and described network output error is the comparison difference of the calculated value of the T-N group width of heating obtained in step 1 and the network output of this step calculating;
Step 3.3, judges whether to reach maximum frequency of training, and according to whether reaching maximum frequency of training selection execution following steps:
Select to perform step 1, if not yet reach maximum frequency of training, judge whether network output error is less than anticipation error in step 3.2, if be less than anticipation error, then train end, preserve weights and the threshold value of each layer in reverse transmittance nerve network in step 3.2 simultaneously, obtain forecast model undetermined; If be greater than anticipation error, after revising the weights of each layer in reverse transmittance nerve network and threshold value step repeat 3.2. wherein modifying factor adopt the modifying factor calculated in step 3.2;
Select to perform step 2, if reach maximum frequency of training, then this reverse transmittance nerve network can not be restrained in given frequency of training, and training terminates;
Step 3.4, by the forecast model undetermined in N group test sample book one by one input selection execution step 1, if predicated error is lower than showing during prescribed level that this forecast model undetermined can be used in predicting the minimum width of heating needed for the post weld heat treatment of 9%Cr martensite heat-resistant steel pipeline, namely namely this forecast model undetermined is the forecast model obtained in step 3; Otherwise this forecast model undetermined does not meet, and terminates whole step.
In the present embodiment, training and test refer to and calculate under gained 3650 groups of different conditions in 9%Cr novel martensitic heat-resisting steel pipeline post weld heat treatment width of heating data 3600 groups as training sample to set up model training with finite element software above, test as test sample book by 9%Cr novel martensitic heat-resisting steel pipeline post weld heat treatment width of heating data under 50 groups of different conditions of remainder to the BP network trained.Error backpropagation algorithm is adopted to train to network model network, training flow process as shown in Figure 2, deconditioning is got final product when the output error of neural network reaches 0.5mm after repetition training, training error figure as shown in Figure 3, when neural network to the predicated error of 50 groups of test sample books lower than showing during prescribed level that network model can be used for prediction 9%Cr novel martensitic heat-resisting steel posted sides pipeline post weld heat treatment width of heating.
Step 4, Modifying model module, revises in conjunction with the forecast model of 9%Cr martensite heat-resistant steel posted sides pipeline post weld heat treatment measured data of experiment to gained, correction model output layer threshold values;
Step 5, width of heating optimizes module, and analysis conduit size (caliber and wall thickness), heat treatment environment temperature, control temperature, the default inside and outside wall temperature difference, be input to the minimum width of heating that revised model can obtain pipeline post weld heat treatment.
Choose line size (caliber and wall thickness) in the present invention, preset the inside and outside wall temperature difference, heat treatment environment temperature and control temperature as input parameter, applicable scope is as follows:
Internal diameter of the pipeline (radius): 100mm-500mm;
Pipeline wall thickness: 30mm-140mm;
Preset the inside and outside wall temperature difference: 0 DEG C-50 DEG C;
Heat treatment environment temperature :-10 DEG C-30 DEG C;
Control temperature: 750 DEG C-780 DEG C.
embodiment:
The inner and outer walls of pipeline temperature difference size of BP Neural network optimization involved in the present invention and actual measurement contrasts:
Analyze and the 9%Cr martensite heat-resistant steel pipeline line size (caliber and wall thickness) of kind of the specification of three shown in record sheet 1, heat treatment environment temperature, control temperature and the default inside and outside wall temperature difference, the numerical value of each influence factor is input in model and calculates, the minimum width of heating of 9%Cr martensite heat-resistant steel pipeline post weld heat treatment under this condition can be calculated fast.Additionally by experiment with the precision verifying this model.In this example with the result of gained of the present invention and measured result as shown in table 2 below.
Table 1
?the post weld heat treatment parameter of 9%Cr martensite heat-resistant steel pipeline
| Pipeline specifications/mm | Width of heating/mm | Insulation width/mm | Environment temperature/ | Control temperature/ | The default inside and outside wall temperature difference/ |
| ID296*65 | 510 | 750 | 13 | 755 | 31 |
| ID430*90 | 937 | 1137 | 10 | 756 | 33 |
| ID288*110 | 866 | 1066 | 15 | 765 | 32 |
Table 2 adopts the inventive method and measured data to compare
| Pipeline specifications/mm | The inventive method/ | Measured value/ | Error/ |
| ID296*65 | 515 | 510 | 5 |
| ID430*90 | 930 | 937 | -7 |
| ID288*110 | 870 | 866 | 3 |
Result of calculation shows, with the present invention propose 9%Cr novel martensitic heat-resisting steel posted sides pipeline post weld heat treatment width of heating optimization method computed information and experimental data more consistent, width of heating Error Absolute Value is less than 10mm.Obviously there is plurality of advantages compared with experimental technique, except calculating 9%Cr novel martensitic heat-resisting steel posted sides pipeline post weld heat treatment width of heating quickly and easily, optimizing beyond Technology for Heating Processing, the difference problem of domestic and international heat treatment technics code can also be solved.
Specific embodiment described herein is only to the explanation for example of the present invention's spirit.Those skilled in the art can make various amendment or supplement or adopt similar mode to substitute to described specific embodiment, but can't depart from spirit of the present invention or surmount the scope that appended claims defines.
Claims (4)
- The optimization method of 1.9%Cr martensite steel pipeline post weld heat treatment width of heating, is characterized in that, comprise the following steps:Step 1, calculate the post weld heat treatment calculation model for temperature field of T group different size pipeline under different heating width, different heat treatment environment temperature, different control temperature by Temperature calculating module, adopt finite element analysis software to calculate the post weld heat treatment inside and outside wall temperature difference of each group model;Step 2, by neural network module in conjunction with any specification pipeline different heat treatment environment temperature, different control temperature and different preset inside and outside wall temperature difference condition under, width of heating minimum needed for pipeline; Set up based on error backward propagation method;Step 3, forecast model sets up module, the data obtaining T group width of heating for step 1 carry out training and testing in step 2 based on error backward propagation method, obtain the forecast model that can be predicted 9%Cr martensite heat-resistant steel posted sides pipeline post weld heat treatment width of heating;Step 4, Modifying model module, revises in conjunction with the forecast model of 9%Cr martensite heat-resistant steel posted sides pipeline post weld heat treatment measured data of experiment to gained;Step 5, width of heating optimizes module, and analysis conduit size, heat treatment environment temperature, control temperature, the default inside and outside wall temperature difference, be input to the minimum width of heating that revised model can obtain pipeline post weld heat treatment;In described step 1, calculate the heat place calculation model for temperature field of T group different size pipeline under different heating width, different heat treatment environment temperature, different control temperature, pipeline post weld heat treatment inside and outside wall temperature extent under utilization finite element software calculating different condition, concrete grammar is:According to the applicable cases of 9%Cr martensite steel pipeline, choose line size scope; According to domestic and international heat treatment technics code, pipeline is calculated to the size of heating tape width, insulation width, choose width of heating scope, insulation width is chosen according to electric power standard; According to control temperature and the heat treatment environment temperature conditions of 9%Cr martensite steel pipeline, select the scope of control temperature and heat treatment environment temperature, set up T group 9%Cr martensite steel pipeline post weld heat treatment temperature field theoretical calculation model, by using finite element software to calculate line size, width of heating, control temperature and heat treatment environment temperature to the impact of equivalent point position, computing method are as follows:Step 1.1, in finite element software, sets up 9%Cr martensite steel pipeline post weld heat treatment calculation model for temperature field;Step 1.2, definition starting condition, boundary condition, solve;Step 1.3, after having calculated, checks inner-walls of duct temperature and outside wall temperature in preprocessor, calculates inside and outside wall temperature extent.
- 2. the optimization method of 9%Cr martensite steel pipeline post weld heat treatment width of heating according to claim 1, is characterized in that, in described step 2, the concrete grammar set up based on error backward propagation method is:Step 2.1, definition input layer and output layer:Choose caliber, wall thickness, preset the numerical value of the inside and outside wall temperature difference, control temperature and heat treatment environment temperature as input variable, therefore the neuron number of this network input layer is 5; Minimum width of heating required under different condition is as the output of network model, and therefore output layer neuron number is 1;Step 2.2, selects hidden layer number and Hidden unit number: adopt single hidden layer, and determine that the number of hidden nodes is 10;Step 2.3, the determination of other parameters: the transport function of hidden layer is unipolarity S type function: f (x)=1/ (1+e -x), the transport function of output layer is linear function: f (x)=x, makes network export any value, and frequency of training is 1800 times, and error target is 0.5, and selection sample number is T, wherein N number of test sample book, T-N training sample.
- 3. the optimization method of 9%Cr martensite steel pipeline post weld heat treatment width of heating according to claim 1, it is characterized in that, in described step 2, an input layer, a middle layer and an output layer is comprised based on error backward propagation method, input layer has 5 neurons, there are 10 neurons in middle layer, and output layer has 1 neuron; The transport function in the middle layer of described forecast model is unipolarity S type function, and the transport function of output layer is linear function, makes network export any value; T width of heating is obtained to step 1 as follows to carrying out the concrete steps of training and testing based on error backward propagation method in step 2:Step 3.1, setting weights and threshold and frequency of training, and initialization is carried out to weights and threshold, win T-N group sample in T group sample at random as training sample, N group sample is as test sample book, input T-N group training sample, described sample is the size of the T group width of heating obtained in step 1 and the influence factor of the minimum width of heating of T group 9%Cr martensite heat-resistant steel pipeline post weld heat treatment;Step 3.2, computational grid exports, and obtains weights and the threshold value of each layer in reverse transmittance nerve network, and calculates the weights of each layer and the modifying factor of threshold value in reverse transmittance nerve network, according to the T-N group A obtained in step 1 1temperature calculations and network export computational grid output error, and described network output error is the comparison difference of the calculated value of the T-N group width of heating obtained in step 1 and the network output of this step calculating;Step 3.3, judges whether to reach maximum frequency of training, and according to whether reaching maximum frequency of training selection execution following steps:Select to perform step 1, if not yet reach maximum frequency of training, judge whether network output error is less than anticipation error in step 3.2, if be less than anticipation error, then train end, preserve weights and the threshold value of each layer in reverse transmittance nerve network in step 3.2 simultaneously, obtain forecast model undetermined; If be greater than anticipation error, after revising the weights of each layer in reverse transmittance nerve network and threshold value step repeat 3.2. wherein modifying factor adopt the modifying factor calculated in step 3.2;Select to perform step 2, if reach maximum frequency of training, then this reverse transmittance nerve network can not be restrained in given frequency of training, and training terminates;Step 3.4, by the forecast model undetermined in N group test sample book one by one input selection execution step 1, if predicated error is lower than showing during prescribed level that this forecast model undetermined can be used in predicting the minimum width of heating needed for the post weld heat treatment of 9%Cr martensite heat-resistant steel pipeline, namely namely this forecast model undetermined is the forecast model obtained in step 3; Otherwise this forecast model undetermined does not meet, and terminates whole step.
- 4. the optimization method of 9%Cr martensite steel pipeline post weld heat treatment width of heating according to claim 1, it is characterized in that, in described step 4, the data of 9%Cr martensite steel pipeline post weld heat treatment experiment measuring and model calculation value are analyzed, and correction model exports threshold value.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101076609A (en) * | 2004-11-09 | 2007-11-21 | 谢夫勒两合公司 | Method for thermally treating a component consisting of a fully hardenable, heat-resistant steel and a component consisting of said steel |
| CN101424610B (en) * | 2008-11-14 | 2010-12-22 | 江苏大学 | Nitrogen austenite steel microstructure predicting method |
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-
2012
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101076609A (en) * | 2004-11-09 | 2007-11-21 | 谢夫勒两合公司 | Method for thermally treating a component consisting of a fully hardenable, heat-resistant steel and a component consisting of said steel |
| CN101424610B (en) * | 2008-11-14 | 2010-12-22 | 江苏大学 | Nitrogen austenite steel microstructure predicting method |
Non-Patent Citations (3)
| Title |
|---|
| 9%～12%Cr马氏体耐热钢母材及焊缝的硬度控制;李益民等;《热力发电》;20100331;第39卷(第3期);第57-60、81页 * |
| 人工神经网络预测奥氏体化温度;侯哲哲等;《石家庄铁道学院学报》;20050630;第18卷(第2期);第61-63页 * |
| 用人工神经网络模型预测钢的奥氏体形成温度;由伟等;《金属学报》;20041130;第40卷(第11期);第1133-1137页 * |
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